Neural Network Approach to Estimate Fetal Weight in the Late Third Trimester of Pregnancy

神经网络方法估计妊娠晚期胎儿体重

• 批准号: 10685346
• 负责人: Caitlin Dreisbach
• 金额: $ 15.88万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-17 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685346
• 关键词: 37 weeks gestation; Abdomen; Agreement; Algorithmic Analysis; Algorithms; Anatomy; Award; Biometry; Birth; Birth Certificates; Birth Weight; Blinded; Body mass index; Cardiology; Cesarean section; Characteristics; Clinical; Clinical assessments; Data; Data Science; Decision Making; Detection; Development; Diagnosis; Diameter; Discipline of Nursing; Discipline of obstetrics; Environment; Eye diseases; Femur; Fetal Weight; Fingers; Future; Gestational Age; Goals; Growth; Head circumference; Height; Hospitals; Image; Image Analysis; Infant; Infant Development; Information Systems; Interdisciplinary Study; Intervention; Learning; Length; Manuals; Maternal-fetal medicine; Measurement; Medicine; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Midwife; National Institute of Nursing Research; Neural Network Simulation; Newborn Infant; Nurses; Nursing Assessment; Observational Study; Operative Surgical Procedures; Overweight; Palpation; Parietal; Participant; Patients; Pattern Recognition; Perinatal; Personal Satisfaction; Physical Examination; Physicians; Policies; Practice Guidelines; Pregnancy; Pregnant Women; Prenatal care; Provider; Radiology Specialty; Research; Research Personnel; Role; School Nursing; Signal Transduction; Specificity; Technology; Third Pregnancy Trimester; Time; Training; Translations; Ultrasonography; United States; Universities; Uterus; Weight; Woman; Women' s Health; antenatal; breast density; career; career development; clinical care; clinical decision-making; computer science; computing resources; convolutional neural network; effectiveness testing; fetal; improved; multimodality; neonate; neural network; neural network algorithm; neurodevelopment; policy implication; prospective; research study; skill acquisition; skills; success; tool; tumor; ultrasound

Project Summary and Abstract Fetal weight estimation, or the assessment of antenatal fetal weight for the purposes of growth tracking and labor planning, is a critical component of safe prenatal care. Estimations currently rely on ultrasound-derived measurements of specific fetal planes to indirectly assess growth and wellbeing. The standard fetal biometric measurements for the estimation of fetal weight (biparietal diameter, head circumference, abdominal circumference and femur length) are poorly correlated to actual fetal weight, defined as the measurement of newborn weight in grams at birth. For newborns who are above 4,000 grams at birth, current error estimates of fetal weight in the late-third trimester of pregnancy are only accurate approximately 40% of the time. By no longer relying on fetal biometric measurements, data science approaches have the potential to estimate fetal weight with lower bias and errors compared to standard regression methods. To date, no studies have used ultrasound images, not just the fetal measurements, as input into a neural network approach to estimate fetal weight. The overarching goal of this proposal is to develop the skills and training necessary to lead the advancement of data science for use in clinical assessment during pregnancy. Using existing ultrasound imaging and birth certificate data (n=17,478 patients) from the University of Rochester (UR) Medicine Hospitals and the Finger Lakes Regional Perinatal/Obstetrics Data System (PDS), and n= 310 patients in the R01 study, Understanding Pregnancy Signals and Infant Development (UPSIDE: R01HD083369), the specific aims are: 1) To determine the maternal (i.e., body mass index) and fetal factors (i.e., growth measurements) that increase the discordance between the estimation of fetal weight by the Hadlock formula and actual birth weight of neonates using birth certificate data from the PDS, 2) To evaluate the accuracy of a CNN algorithm on ultrasound images in the third trimester to estimate fetal weight compared to the Hadlock formula, and 3) To test the effectiveness CNN algorithm on new ultrasound images from the UPSIDE study. This proposal will leverage the expertise of Dr. Caitlin Dreisbach’s mentorship team, computational resources, and the exceptional research environment at the UR School of Nursing, Goergen Institute for Data Science, and the Rochester Institute of Technology. Results from this study have the potential to change practice and improve clinical assessments during the late third trimester of pregnancy. The research study and mentored training included in this award allows Dr. Dreisbach to establish her long-term career goal of becoming an independent investigator with expertise in the translation of data science to obstetric clinical care.

项目概要和摘要 胎儿体重估计，或出于生长跟踪和评估目的的产前胎儿体重评估 分娩计划是安全产前护理的重要组成部分。目前的估计依赖于超声波得出的 测量特定胎儿平面以间接评估生长和健康状况。标准胎儿生物识别 用于估计胎儿体重的测量（双顶径、头围、腹围） 周长和股骨长度）与实际胎儿体重（定义为测量 新生儿出生时的体重（以克为单位）。对于出生时体重超过 4000 克的新生儿，目前的误差估计为 妊娠晚期胎儿体重的准确率大约只有 40%。没有 不再依赖胎儿生物识别测量，数据科学方法有可能估计胎儿 与标准回归方法相比，具有较低偏差和误差的权重。迄今为止，还没有研究使用 超声图像，而不仅仅是胎儿测量值，作为神经网络方法的输入来估计胎儿 重量。该提案的总体目标是培养领导团队所需的技能和培训 用于妊娠期间临床评估的数据科学的进步。使用现有的超声波 来自罗切斯特大学 (UR) 医学医院的影像和出生证明数据（n=17,478 名患者） 和 Finger Lakes 地区围产期/产科数据系统 (PDS)，以及 R01 研究中的 n= 310 名患者， 了解妊娠信号和婴儿发育（UPSIDE：R01HD083369），具体目标是：1） 确定增加体重的母体因素（即体重指数）和胎儿因素（即生长测量值） Hadlock 公式估算的胎儿体重与实际出生体重之间的不一致 新生儿使用 PDS 的出生证明数据，2) 评估 CNN 算法的准确性 与 Hadlock 公式相比，使用妊娠晚期的超声图像来估计胎儿体重，以及 3) 在 UPSIDE 研究的新超声图像上测试 CNN 算法的有效性。该提案将 利用 Caitlin Dreisbach 博士导师团队的专业知识、计算资源和 UR 护理学院、格尔根数据科学研究所和 罗切斯特理工学院。这项研究的结果有可能改变实践并改进 妊娠晚期的临床评估。研究性学习和指导培训 纳入该奖项使 Dreisbach 博士能够确立成为独立的长期职业目标 具有将数据科学转化为产科临床护理的专业知识的研究者。